High-Resolution ROMA Analysis of Genome Copy Number Variation in the HapMap

描述（由申请人提供）：根据我们小组和其他人最近发表的研究，发现在细胞遗传学水平上不可见的大规模DMA拷贝数变异（CNVs）是人类基因组普遍存在的特征。我们的研究结果表明，平均而言，两个个体有12个CNVs的差异，涉及3 Mb或大约0.1%的基因组。这与0.1%的遗传差异是由单核苷酸多态性（snp）造成的相当。然而，与核苷酸序列变异（如snp）相比，基因组中的结构变异尚未得到很好的表征。关于这些结构变异的基因组位置、频率和稳定性，以及它们在人类进化和遗传疾病中的重要性，还有很多有待了解。为了进一步开展这方面的研究，有必要通过表征大量个体样本和构建验证的cnv数据库来扩展拷贝数变化的现有知识。一个全面的CNVs目录将有助于大规模研究(1)CNVs与疾病风险的关联(2)CNVs对药物治疗反应的影响，以及(3)结构变异在人类进化中的作用。我们建议使用一种强大的高分辨率CNV发现方法，代表性寡核苷酸微阵列分析（ROMA），收集来自国际HapMap项目的270个个体的基因组拷贝数变异的数据资源。我们将使用提供8 kb分辨率的380,000探针阵列执行ROMA扫描。此外，我们将把我们的数据与使用其他CNV发现方法获得的CNV信息进行整合。我们将选择一组600个常见的CNVs（次要等位基因频率>= 1%）进行精细尺度表征，并使用平铺路径寡核苷酸阵列以更高的分辨率定义常见CNVs的边界，分辨率为每5 bp一个探针。对于缺失和重复的进一步子集，我们将在序列水平上表征CNV连接。最后，为了将CNVs整合到基于SNP的HapMap中，我们将识别与CNVs连锁不平衡的SNP标记。所有关于拷贝数变化的信息将通过dbSNP提供，原始微阵列数据将从www.hapmap.org提供。

DESCRIPTION (provided by applicant): Based on recent studies published by our group and others, it was discovered that large-scale DMA copy number variants invisible at the cytogenetic level (CNVs), are a ubiquitous characteristic of the human genome. Our findings indicated that, on average, two individuals differ by a dozen CNVs involving 3 Mb or approximately 0.1 % of the genome. This is comparable to the 0.1 % of genetic difference that is due to single nucleotide polymorphisms (SNPs). However, in contrast to nucleotide sequence variants such as SNPs, structural variation in the genome has not been well characterized. Much remains to be learned about the genomic locations, frequency, and stability of these structural variants and their importance in human evolution and genetic disease. To enable further research in this are it is necessary to expand the current knowledge of copy number variation by characterizing a large sample of individuals and constructing a database of validated CNVs. A comprehensive catalog of CNVs will facilitate large-scale studies of (1) the association of CNVs with disease risk (2) the effects of CNVs on response to drug treatment, and (3) the role of structural variation in human evolution. We propose to collect a data resource on genome copy number variation on 270 individuals from the international HapMap project using a powerful high-resolution CNV discovery method, Representational Oligonucleotide Microarray Analysis (ROMA). We will perform ROMA scans using a 380,000 probe array that provides a resolution of 8 kb. In addtion, we will integrate our data with CNV information obtain using other CNV discovery methods. We will select a set of 600 common CNVs (minor allele frequency >= 1%) for fine-scale characterization, and the boundaries of common CNVs will be defined at higher resolution using a tiling path Oligonucleotide array with a resolution of one probe every 5 bp. For a further subset of deletions and duplications, we will characterize the CNV junctions at the sequence level. Lastly, in order to integrate CNVs into the context the SNP-based HapMap, we will identify SNP markers that are in linkage disequilibrium with CNVs. All information on copy number variation will be made available through dbSNP and raw microarray data will be made available from www.hapmap.org .